The goal of the proposed effort is to develop and make available to the scientific community a modular, integrated, multiscale computational modeling framework that will allow the user to design safe and effective peripheral neurostimulators. The multiscale computational framework is based on the seamless integration of multiple computational modules/platforms particularly suited for integration: (a) a multi- resolution, frequency-domain, large-scale electromagnetic field modeling platform based upon our Admittance/Impedance Method (AM/IM) for the prediction of fields and currents induced in the neural tissue by arbitrary neurostimulators; (b) micron-resolution computational models of the bulk electrical and magnetic properties of axons and their excitation in peripheral nerve models of mammalians using NEURON software, coupled in space and time to the Admittance/Impedance Method; (c) a computational tool for the estimation of direct, electrically or magnetically-induced, tissue and neural damage due to arbitrary, user-defined, peripheral neurostimulators and waveforms and for the estimation of activity- based early axonal damage (EAD) based on correlation with experimentally observed damage in chronically implanted neurostimulators. The development of the proposed modules will provide the most complete predictive software framework available to assess acute and activity-based safety of peripheral neurostimulators due to parameters including electrode geometric::al features, charge density, charge per phase, frequency of stimulation and thermal increase. To the best of our knowledge, there is no computational method readily available that addresses both the effectiveness of the neurostimulator (modeling of the excitation in peripheral nerve models due to arbitrary electrode geometries and waveforms) and the safety of the neurostimulator both at the large-scale (electromagnetic tissue models of the human body based on high-resolution, dielectric properties- based, discretized computational models) and at micron-resolution (neural level). The proposed effort will consists of a) generation of computational models of peripheral nerves; b) development of the computational modules and platform; and c) experimental verification of the predictive capabilities of the computational models and platform.